{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"block_hidden": true,
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext rpy2.ipython\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python API\n",
"\n",
"Prophet follows the `sklearn` model API. We create an instance of the `Prophet` class and then call its `fit` and `predict` methods. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column must contain a date or datetime (either is fine). The `y` column must be numeric, and represents the measurement we wish to forecast.\n",
"\n",
"As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning). We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances).\n",
"\n",
"First we'll import the data and log-transform the y variable."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from fbprophet import Prophet"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
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"outputs": [
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" | \n",
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" 8.519590 | \n",
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" \n",
" | 2 | \n",
" 2007-12-12 | \n",
" 8.183677 | \n",
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" \n",
" | 3 | \n",
" 2007-12-13 | \n",
" 8.072467 | \n",
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" 2007-12-14 | \n",
" 7.893572 | \n",
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" 2017-01-15 | \n",
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" 2017-01-17 | \n",
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\n",
" \n",
" | 3268 | \n",
" 2017-01-18 | \n",
" 8.150081 | \n",
" 7.440242 | \n",
" 8.906296 | \n",
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\n",
" \n",
" | 3269 | \n",
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